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Chapter 15. Computational Biophysics
Computational Biophysics
RLE Group
Computational Biophysics Group
Academic and Research Staff
Professor Collin Stultz
Graduate Students
Charles Fisher
Elaine Gee
Austin Huang
Paul Nerenberg
Christine Phillips
Ramon Salsas-Escat
Joyatee Sarker
Orly Slavin
Zeeshan Syed
Visiting Graduate Students
Madhavi Gavini
Thomas Gurry
Undergraduate Students
Anjali Muralidhar,
Steven Pennybaker
Collaborators
Prof. Catherine Drennan (MIT Chemistry)
Prof. John Guttag (MIT, EECS)
Prof. D. E. Ingber (Harvard Medical School)
Prof. Barbara Imperiali (MIT Chemistry)
Prof. Charlie Sodini (MIT, EECS)
Prof. Anantha Chandrakasan (MIT, EECS)
Technical and Support Staff
Arlene Wint
Introduction/Group Overview
The RLE Computational Biophysics Group is focused on understanding conformational changes in
biomolecules that play an important role in common human diseases. The group uses an interdisciplinary
approach combining computational modeling with biochemical experiments to make connections between
conformational changes in macromolecules and disease progression. By employing two types of
modeling, molecular dynamics and probabilistic modeling, hypotheses can be developed and then tested
experimentally.
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Chapter 15. Computational Biophysics
1. The Structure of Collagen and Collagenolysis
Sponsors:
National Science Foundation award no. MCB -- 0745638, Burroughs Wellcome Fund Contract
no.1004339.01, W.M. Keck Foundation,
Project Staff:
Paul Nerenberg, Prof. Collin Stultz, Thomas Gurry
1.1 The Mechanism of Collagenolysis: A Substrate-centric View
Collagenolysis (collagen degradation) is a critical process in the progression of cancer metastasis,
atherosclerosis, and other diseases. Despite considerable efforts to understand the steps involved, the
exact mechanism of collagenolysis remains unknown. We have proposed a degradation mechanism in
which collagen exists in multiple states, some featuring structures that are unwound in the vicinity of the
collagenase cleavage site, and that collagenases preferentially bind to and stabilize the unwound
structures before degradation occurs.
The focus of this work is to investigate several aspects of this degradation mechanism using both
experimental and computational methods. In particular, we have used molecular dynamics simulations to
explore the structure of human type I collagen in the vicinity of the collagenase cleavage site.
Simulations of type I collagen in this region suggest that partial unfolding of the α2 chain is energetically
preferred relative to unfolding of the α1 chains. Localized unfolding of the α2 chain leads to a metastable
structure that has disrupted hydrogen bonds N-terminal to the collagenase cleavage site. Our data
suggest that this disruption in hydrogen bonding pattern leads to increased chain flexibility, thereby
enabling the α2 chain to sample different partially unfolded states. We are currently testing this
hypothesis experimentally by using the catalytic domains of collagenases as a probe for partially unfolded
states.
We are also conducting NMR experiments with model peptides of type III collagen, another fibril-forming
collagen present in many of the same tissues as type I collagen. The overall goal of these experiments is
to quantify the ratio of folded to partially unfolded states in the vicinity of the collagenase cleavage site, as
well as in the vicinity of the four pseudo-cleavage sites (i.e., sites that potentially could be cleaved by
collagenases, but normally are not). These experiments will enable us to test a related hypothesis that
the primary difference between the real cleavage site and the various pseudo-cleavage sites is the extent
of unfolding in the vicinity of the scissile bond.
Figure 1: Potential of mean force for chain C (α2) of type I collagen showing both native and vulnerable states.
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Figure 2: (a) Representative structure from the native state of type I collagen. The α2 chain is colored in red. (b)
Interchain NO distances of the native state of type I collagen. The triplets containing the scissile bonds are colored in
magenta. (c) Representative structure of the vulnerable state of type I collagen. (d) Interchain NO distances of the
vulnerable state of type I collagen. NO distances greater than 4 Å are highlighted in orange.
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1.2 The Contribution of Salt Bridges to Triple-Helical Stability
Conformational stability is a critical property of the collagen triple helix. It is in part ensured by the
presence of a repetitive GXY sequence motif, with a high frequency of proline residues in the X position
and hydroxyproline residues in the Y position. A high natural abundance of lysine residues in the Y
position followed by an acidic residue in the following X position also occurs in collagen-like sequences.
We hypothesized that these sequences result in inter-chain electrostatic interactions which stabilize the
collagen triple helix.
To investigate this hypothesis, we performed umbrella sampling molecular
dynamics simulations of these sequences hosted in the model peptides (GPO)3 GPKGEO(GPO)3 and
(GPO)3 GPKGDO(GPO)3. By constructing potentials of mean force as a function of the inter-chain lysineNζ to glutamate-Cδ/aspartate-Cγ inter-atomic distances, we showed that in all but the chain C to chain A
interaction in GPKGDO, energy minima in the potentials of mean force correspond to salt-bridge
conformations between the lysine and acidic residues (Figure 1A). At larger inter-atomic distances,
where the electrostatic interactions can be considered broken, free energies from the potentials of mean
force are significantly higher than those of the salt-bridge states, conclusively showing that these
interactions stabilize the triple helical structure of collagen (Figure 1B). These findings suggest that interchain salt-bridges could play crucial roles in stabilising collagen triple helices, particular in hydroxprolinedeficient regions and in organisms lacking the prolyl hydroxylase enzyme.
Figure 1 - (A) Potential of mean force for the GPKGDO chain A to chain B interaction. (B) Structural representation of
the A-B salt bridge.
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2. The Role of Unfolded States in Collagen Degradation
Sponsors:
HST, RLE, Burroughs Wellcome Fund Contract no. 1004339.01, W.M. Keck Foundation, Merck
Foundation
Project Staff:
Ramon Salsas-Escat, Prof. Collin Stultz, Steven Pennybaker
2.1 The Role of Unfolded States in Collagen Degradation
Matrix Metalloproteases (MMPs) cleave native collagen at a single site despite the fact that collagen
contains more than one scissile bond that can, in principle, be cleaved (Figure 1). For peptide bond
hydrolysis to occur at one specific site, MMPs must: 1) localize to a region near the unique scissile bond;
2) bind residues at the catalytic site that form the scissile bond; and 3) hydrolyze the corresponding
peptide bond. Prior studies suggest that for some types of collagen, binding of non-catalytic MMP
domains to amino-acid sequences in the vicinity of the true cleavage site facilitates the localization of
collagenases. In the present study, our goal is to determine whether binding to the catalytic site also plays
a role in determining MMP specificity. To investigate this, we computed the conformational free energy
landscape of type III collagen at each potential cleavage site (Figure 2). The free energy profiles suggest
that while all potential cleavage sites sample unfolded states at relatively low temperatures, the true
cleavage site samples structures that are more vulnerable (i.e. the scissile bond is more solvent exposed,
Figure 3) and complementary to the catalytic site (they dock best into the active site of an MMP, Figures 4
and 5). By contrast, potential cleavage sites that are not cleaved sample states that are less vulnerable
and that are relatively incompatible with the MMP active site. These data imply that locally unfolded
potential cleavage sites in type III collagen sample distinct unfolded ensembles, and that the region about
the true collagenases cleavage site samples states that are most complementary to the MMP active site.
Figure 1: Sequence corresponding to the 5 potential cleavage sites in type III collagen. The true collagenase
cleavage site (defined by the triplet GIA) is colored in green, while potential cleavage sites (three GLA triplets and
one GIA triplet), that are not cleaved, are colored in red. The actual scissile bond is depicted with a ~ sign, while the
scissile bonds corresponding to triplets that are not cleaved are depicted with a * sign.
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Chapter 15. Computational Biophysics
Figure 2: Potential of mean force (pmf) plots for the five potential cleavage sites in type III collagen (sequences listed
in Fig. 1). Representative structures from each energy minimum are shown. The four atoms comprising the scissile
bond (H, N, C and O) are shown in green for the true cleavage site and in red for the four potential cleavage sites.
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Figure 3: Vulnerability Score plots for the 5 cleavage sites in type III collagen. Vulnerability values are expressed as
a percentage, with 100% being the vulnerability of the scissile bond of a G-I or GL dipeptide free in solution.
Figure 4: Results of docking studies. RMS values are computed between the backbone atoms of the collagen-like
peptide in the co-crystal structure of MMP8 and the corresponding backbone atoms in the chain A of the triple helical
peptide. All 300 structures from each energy minimum shown in Figure. 2 were individually docked into CMMP8.
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Chapter 15. Computational Biophysics
Figure 5: A) Overview of the best docked structure of S1 into the catalytic site of CMMP8. Chain A, which is unfolded
in our simulations, is shown in stick representation, while chains B and C are colored in red. The structure of CMMP8
is transparent.
B) The catalytic site of the bound complex with chain A present (chains B and C deleted). The catalytically important
E198 is shown, together with the 3 histidine residues that coordinate the Zn+2 ion. The isoleucine side chain sits in the
S1’ site, while the alanine side chain sits in the S2’ site.
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2.2 A Virtual screens for molecules that modulate collagen stability
Triple-helical collagen is the most abundant protein in all mammals. It is hypothesized that naturally,
collagen samples partially unfolded states. These states are accessed when one strand partially unwinds
from the triple-helix. When in this unfolded state, collagen is susceptible to cleavage by Matrix
metalloproteases (MMPs). MMPs play a role in regulating multiple biological functions, but when levels of
collagen degradation are abnormally high, they can also play a role in multiple diseases, such as
atherosclerosis and cancer. The goal of my work is to find a small molecule which can bind to, and
stabilize, collagen in its folded form. I am using AutoDock, a molecular docking simulator, to screen for
molecules received from the ZINC database that have this potential and will then be testing them in vitro.
The attached picture is of a potential small molecule bonded to the triple-helical collagen.
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Chapter 15. Computational Biophysics
3. Modeling the Unfolded State of Tau Protein
Sponsors:
HST, RLE, Jonathan Allen Junior Faculty Award, Westaway Research Fund; NIH R21
Project Staff:
Madhavi Gavini, Austin Huang, Prof. Collin Stultz
3.1 Title Needed for Madhavi’s contribution
I computationally studied the monomeric structure of amyloid beta, a protein that forms amyloid plaques
in the brains of individuals with Alzheimers. A library of structures was generated using replica exchange
and these structures where then clustered based on potential energy and RMSD to derive a set of
representative structures. These structures were then analyzed and their predicted scalar coupling
constants and chemical shifts were compared to experimentally derived values to establish a baseline
which will be used as a starting point to develop semi-empirical models which may more accurately model
theoretical simulations after experimental conditions. I also computationally investigated whether a
disorder predicting algorithm, PONDr is effective in predicting intrinsically disordered sites for nonglobular proteins that require multiple subunits to form a complete structure. It was found that this
algorithm was not as effective in determining disordered sites for linear proteins that require multiple
subunits such as collagen.
The attached figure shows one of the representative structures derived using the clustering algorithm
developed that takes into consideration both RMSD and energy.
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Chapter 15. Computational Biophysics
4. Intrinsically Disordered Proteins
Sponsors:
HST, RLE, Burroughs Wellcome Fund Contract no. 1004339.01, Westaway Research Fund, NIH R21
Project Staff:
Orly Slavin, (Veena Venkatachalam), Austin Huang, Prof. Collin Stultz
4.1 Instrinsically Disordered Proteins
Intrinsically disordered proteins (IDPs) comprise a large group of biopolymers that do not adopt welldefined three dimensional structures under physiological conditions. α-Synuclein is one such intrinsically
disordered protein that forms organized fibrils composed, mainly of β- structure. The medical significance
in studying the α-Synuclein protein is vast. This protein has been implicated in the pathogenesis of
several neurodegenerative diseases, including Alzheimer's disease, and Parkinson's disease (PD)
associated dementia with Lewy bodies.
We develop structural models of α-Synuclein that accurately represent the ensemble of conformations
that are accessible to the protein under physiologic conditions. Our overarching hypothesis is that the
unfolded state of IDPs can be modeled as a set of energetically favorable conformations where each
conformation corresponds to a local energy minimum on a complex potential energy surface. We
construct the conformations that contribute to the ensemble that represent the unfolded state by using a
computation method called Energy Minima Mapping that was developed in the Computational Biophysics
Group. These ensembles are formed using experimental data such as chemical shifts and residual
dipolar coupling. As a first step we used molecular dynamics simulations to construct the multiple possible
configurations. We first divide the protein into overlapping segments; following this we find possible
conformations for each segment. Last, we use clustering algorithms to combine the segments into
possible conformations for the entire protein.
Clustering tree for a representative eight amino acids long segment, using the side chains as a criteria to
decide which conformations share spatial similarity
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Chapter 15. Computational Biophysics
5. Computational Studies of Nickel Regulatory Protein NikR
Sponsors:
HST, RLE, Jonathan Allen Junior Faculty Award, Drennan’s
Project Staff:
Christine Phillips, Anjali Muralidhar, Prof. Catherine Drennan, Prof. Collin Stultz
5.1 NikR: A Brief Introduction to the Problem
Regulation of transition metals in the cell is crucial for all living organisms. While some enzymes require
metals to function properly, an excess of metal ions in the cell can induce apoptosis. In bacteria, the
transcription factor NikR regulates the concentration of nickel ions within the cell. Biochemical studies
have shown that Escherichia coli NikR binds to DNA with a nanomolar affinity when stoichiometric nickel
is present, but with picomolar affinity when excess nickel ions are present (1). Our computational studies
aim to determine whether there is a single nickel site or a series of sites that account for the increased
affinity and to examine the possible location(s) of these sites.
There are two proposed possibilities for the location(s) of these excess sites. The first possibility is at the
interface of the central portion of the structure and the DNA binding portion (shown in Fig. 1 in pink
spheres). However, our recently published work shows that this site prefers the cellularly abundant K+ ion
over the divalent Ni2+ ion for both electrostatic and size reasons (2).
90°
Rotation
Figur
e 1. Metal binding sites on NikR. Protein is represented in ribbons in red, green, blue, and yellow with DNA in
orange. Extra nickel binding sites on the surface of NikR with nickel ions represented in blue mesh. Potassium ions
are shown in pink (3).
Crystallographic studies of EcNikR bound to DNA with excess nickel present indicated that excess nickel
ions did not bind in a single specific site, but bound to unconserved histidines on the surface of the
protein (Fig. 1) (3). The second proposal for the location of these excess nickel binding sites is at these
locations on the surface of the protein (shown in Fig. 1 in mesh). Using Poisson-Boltmann directed
electrostatic computations we will probe these potential excess nickel sites seen in the excess nickelsoaked structure to determine which, if any, of these sites is responsible for the increased NikR-DNA
affinity.
Applying computational methods to the excess nickel question
With these calculations, we would like to investigate if the nickel sites on the surface of the protein in the
excess-nickel soaked structure can account for the increase in NikR-DNA binding affinity. We will
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Chapter 15. Computational Biophysics
specifically determine the difference in free energy change upon NikR-DNA binding with stoichiometric
nickel (DGStoic) and NikR-DNA binding with excess nickel (DGExcess) (Fig. 2).
One way to calculate this difference is to determine the change in free energy upon binding DNA with
excess nickel (Fig. 2, DGExcess) and similarly with stoichiometric nickel (Fig. 2, DGStoic). Subtracting these
changes in free energy we can determine the effect excess nickel has on DNA binding affinity (DDG =
DGExcess – DGStoic). We can simplify this calculation using the Poisson Boltzmann formalism and simply
calculate the difference in free energy between the stoichiometric and excess nickel-bound states (Fig.2,
D G). If DDG is negative, then the excess nickel sites we include do increase NikR’s affinity for DNA,
while a positive DDG value would indicate that nickel binding in these sites does not increase NikR’s
affinity for DNA.
Figure 2. Thermodynamic box illustrating our technique for computational analysis of the effect of the excess nickel
binding sites.
From the Poisson Boltmann calculations we can also determine the impact each amino acid has on the
overall free energy difference of the structures and therefore which amino acids may be crucial for
explaining the difference in DNA binding affinity of stoichiometric and excess nickel bound NikR.
References
1
Bloom, S. L., and Zamble, D. B. (2004) Metal-Selective DNA-Binding Response of Escherichia
coli NikR, Biochemistry 43, 10029-10038.
2
Phillips, C. M., Nerenberg, P. S., Drennan, C. L., and Stultz, C. M. (2009) Physical Basis of MetalBinding Specificity in Escherichia coli NikR, J Am Chem Soc 131, 10220-10228.
3
Schreiter, E. R., Wang, S. C., Zamble, D. B., and Drennan, C. L. (2006) NikR-operator complex
structure and the mechanism of repressor activation by metal ions, Proc. Natl. Acad. Sci. U. S. A.
103, 13676-13681.
4
Honig, B., and Nicholls, A. (1995) Classical Electrostatics in Biology and Chemistry, Science 268,
1144-1149.
5
Norberg, J. (2003) Archives of Biochemistry and Biophysics 410, 48-68.
Our recent publications on NikR:
Phillips, C.M., Nerenberg, P.S., Drennan, C.L. and Stultz, C.M. Physical Basis of Metal-Binding Specificity
in Escherichia coli NikR. Journal of the American Chemical Society (2009) 131, 10220-10228.
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Chapter 15. Computational Biophysics
5.2 Investigating the role of excess nickel sites in NikR regulation
I computationally investigated the effects of various factors that can reduce the amount of collagen
degradation that occurs. It was found that the introduction of an enzyme inhibitor, which will have a high
affinity for collagenases prevent collagenases from binding to collagen and causing cleavage, can reduce
degradation at a lower energy than a molecule that will stabilize collagen in its triple helical state and
prevent cleavage. However, it is unclear whether the computationally determined binding energies of the
stabilizing molecule and enzyme inhibitor are realistically achievable. I have also been computationally
analyzing the structure of the regulatory transcription factor, NikR, which regulates the amount of nickel in
a cell. In particular, I have been investigating the structure of NikR in the presence of excess nickel ions
that fill low affinity sites at the surface of the protein. These low affinity sites may be the reason for
increased affinity of NikR for DNA in the presence of excess nickel.
The figure below shows NikR, with potassium ions (purple) stabilizing the NikR-DNA complex, nickel ions
in high affinity sites near the center of the protein (green), and nickel ions in low affinity sites around the
surface of the protein (light blue).
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Chapter 15. Computational Biophysics
6. Understanding Fibronectin Fibrillogenesis by Modeling Physiologic Unfolding of Fibronectin
with Steered Molecular Dynamics Simulation
Sponsors:
NIH (Award No.s NIH/NIGMS Molecular Biophysics Training Grant T32 GM008313 and NIH CA45548),
Keck Foundation (NAKFI Nano 06), Harvard Materials Research Science and Engineering Center,
Department of Defense Innovator Award No. W81XWH-08-1-0659, RLE, Burroughs Wellcome Fund
Contract no. 1004339.01
Project Staff:
Elaine Gee, Prof. D.E. Ingber, Prof. Collin Stultz
6.1 Cell-Mediated Fibronectin Unfolding
When a particular Fibronectin (FN) polymerization is essential for the development and repair of the these
computational simulations.
The extracellular matrix (ECM) controls cell behavior by affecting adhesion, migration, gene expression,
proliferation, and survival through biochemical and mechanical cues. The mature ECM develops from a
provisional fibronectin (FN) matrix, which other ECM molecules, such as collagen, require for deposition.
The process of FN assembly, or FN fibrillogenesis, plays an important role in ECM formation and function
during the development and repair of the extracellular matrix in physiological processes such as
embryogenesis, migration, angiogenesis, and wound healing as well as in pathological conditions as in
cancer. The process of FN assembly requires both integrin binding to FN as well as cell contractility,
which confers strain to the attached molecule. The 10th repeat of FN domain type III (10FNIII) contains
the integrin-binding sequence RGD and has been shown to be mechanically weak. However, the role of
this module in the molecular process of fibrillar assembly remains unclear. By studying the mechanism
by which FN transduces cell-traction forces into the biochemical process of matrix assembly, one can
understand the mechanical principles that define the cell microenvironment. The goal of this project is to
understand how structural unfolding of 10FNIII by traction force affects FN mechanotransduction.
Data suggest that FN fibrillogenesis is facilitated by cell traction forces (on the order of nNs) transferred to
FN via bound membrane integrin receptors. Forces applied to distinct regions of FN drive the formation
of partially unfolded FN intermediates that then bind to additional FN molecules. Past studies have
focused on FN unfolding by applying pulling force at the N- and C-termini of protein fragments rather than
at the site of cell binding. However, since cell membrane integrins bind FN at a surface-exposed RGD
loop on 10FNIII, we investigate the mechanism of cell-mediated FN unfolding by using steered molecular
dynamics to apply tensile force at the RGD site. While pulling at the N- and C-termini of 10FNIII leads to
unfolding trajectories that sample different unfolding pathways as previously published, we find that
pulling at the RGD site unfolds 10FNIII along one well-defined pathway. This pathway accesses a
partially unfolded intermediate structure with solvent exposed hydrophobic N-terminal residues.
Moreover, additional unfolding of this intermediate triggers an arginine residue, which is required for high
affinity cell binding, to separate from the adjacent RGD loop. Based on these results, we propose a
mechanism by which cell traction induces a conformational change in FN that possibly enables FN-FN
associations and cell detachment, thereby promoting the binding of new FN to the cell surface receptor to
promote subsequent cycles of adhesion, unfolding, and fibril formation that drive FN matrix assembly
(Figure 1). Currently, investigations are underway to provide experimental support for the proposed
mechanism obtained from these computational simulations using biophysical approaches including force
spectroscopy techniques and cell-based fluorescence assays.
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Chapter 15. Computational Biophysics
Figure 1. Cell-mediated Fibrillogenesis. The proposed model involves the formation of a partially unfolded
intermediate during cell-traction mediated unfolding. Potential cryptic sites that promote FN aggregation and
fibrillogenesis may exist in this partially unfolded structure. Subsequent unfolding past this intermediate induces the
movement of a crucial arginine (gold bead) at a secondary binding site to a position far from the RGD loop (magenta
bead labels the central glycine). This frees the cell from the RGD ligand and enables it to reattach to another FN
molecule to propagate the process. If 10FNIII refolding is relatively slow, then unfolded portions of this domain are
available to make contacts with other FN molecules to stimulate downstream fibrillogenesis.
Publications:
Gee EPS, Ingber DE, Stultz CM (2008) Fibronectin Unfolding Revisited: Modeling Cell Traction-Mediated
Unfolding of the Tenth Type-III Repeat. PLoS ONE 3: e2373.
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7. ECG Markers to Predict Cardiovascular Death
Sponsors:
HST, RLE, Westaway Research Fund, CIMIT
Project Staff:
Joyatee Sarker, (Zeeshan Syed), Prof. Collin Stultz, Prof. John Guttag
7.1 ECG Measures to Predict Cardiovascular Outcomes
Patients who have had an acute coronary syndrome (ACS) are at a relatively high risk of having
subsequent adverse cardiac events. Several electrocardiographic measures such as heart rate variability,
heart rate turbulence, deceleration capacity, and T-wave alternans have been used to identify patients at
increased risk of adverse cardiac events. An algorithm called dynamic time warping (DTW) aligns heart
beats to minimize differences, producing a morphologic distance (MD) time series. The irregularities in the
MD time series are measured as the morphologic variability (MV). Using holter-ECG data from patients
who were recently admitted with an ACS, we are determining whether morphology based measures can
be used to identify patients at high risk of other adverse cardiac events, such as congestive heart failure,
recurrent ischemia and myocardial infarction.
Coefficients
P-value
age
0.0017
2.33E-06
female
0.0064
0.3608
bmi
0.0002
0.7325
history of diabetes
0.0143
0.0388
history of hypertension
-0.0008
0.9173
current smoker
0.0112
0.1531
prior MI
0.0218
0.0020
history of CHF
0.0080
0.3684
ST segment depression
0.0081
0.2221
SDNN < 50ms
0.0114
0.6249
-0.0379
0.1301
HRVI < 20U
0.0171
0.0291
HRV-LF/HF > 0.95
0.0353
3.02E-05
LVEF < 40%
0.0452
7.29E-06
SDANN < 40ms
MV > 52.5
0.0048
0.5782
Association between clinical and ECG variables and future CHF using a multivariate model.
Patients who died were not included in this model. Significant p-values (< 0.05) are bolded.
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Chapter 15. Computational Biophysics
Publications
Journal Articles, Published
C. Phillips, P. Nerenberg, C. Drennan, C.M. Stultz, “The Physical Basis of Metal Binding Specificity in E.
coli NikR,” Journal of the American Chemical Society 131(29): 10220-10228 (DOI: 10.1021/ja9026314),
(2009).
C. Schubert, and C.M. Stultz, “The Multi-Copy Simultaneous Search Methodology: A Fundamental Tool
for Structure-Based Drug Design,” Journal of Computer Aided Molecular Design (DOI: 10.1007/s10822009-9287-y), (2009).
A. Huang, and C.M. Stultz, “Finding Order within Disorder: Elucidating the Structure of Proteins
Associated with Neurodegenerative Disease,” Future Medicinal Chemistry 1(3): 467-482, (2009).
Z. Syed, P. Sung, B.M. Scirica, D.A. Morrow, C.M. Stultz, and J.V. Guttag, “Spectral Energy of ECG
Morphologic Differences to Predict Death,” Cardiovascular Engineering (DOI 10.1007/s10558-009-90663), (2009).
M. Yoon, V. Venkatachalam, A. Huang, B. Choi, C.M. Stultz, and J.J. Chou, “Residual Structure within the
Disordered C-Terminal Segment of p21Waf1/Cip1/Sdi1 and its Implications for Molecular Recognition,”
Protein Science 18(2): 337-347, (2009).
Z. Syed, P. Sung, B.M. Scirica, S. Mohanavelu, P. Sung, C.P. Cannon, P.H. Stone, C.M. Stultz, and J.V.
Guttag, “Relation of Death within 90 days of Non-St-Elevation Acute Coronary Syndromes to Variability in
Electrocardiographic Morphology,” American Journal of Cardiology, 103(3): 307-311 (DOI:
10.1016/j.amjcard.2008.09.099), (2009).
A. Huang, and C.M. Stultz, “The Effect of a ∆K280 Mutation on the Unfolded State of a MicrotubuleBinding Repeat in Tau,” PLoS Computational Biology 4(8): e1000155 DOI:10.1371/journal.pcbi.1000155.
P.S. Nerenberg, and C.M. Stultz, “Differential Unfolding of α1 and α2 chains in Type I Collagen and
Collagenolysis,” JMB 382, 246-256, (2008).
R. Salsas-Escat, and C.M. Stultz, “Conformational Selection and Collagenolysis in Type III Collagen,”
Proteins: Structure, Function, and Bioinformatics (Accepted article online: Jul 20, 2009) (DOI:
10.1002/prot.22545).
Journal Articles, Accepted for Publication
Z. Syed, C.M. Stultz, M. Kellis, P. Indyk, and J. Guttag, “Motif Discovery in Physiological Datasets: A
Methodology for Inferring Predictive Elements,” ACM Transactions on Knowledge Discovery from Data (In
press).
Journal Articles, Submitted
Gurry T., Nerenberg PS, Stultz CM, The Contribution of Salt Bridges to Triple-Helical Stability. Under
review.
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